In the recent years, magnetic cards and IC cards have been widely used in combination with computer equipment. With the above cards, customer databases have been developed and maintained in various industries such as department stores, specialty boutiques, consumer electronics retailers and super markets. The above databases include customer characteristic information such as names and addresses as well as other information such as accumulated purchase data. Similarly, transactions are maintained in the databases for the financial industry while data called call detail data are maintained in the databases for the telecommunication industry. For example, the call detail data include a caller number, a recipient number and call duration for each call. Based upon the above described databases, one exemplary service is Customer Relationship Management (CRM) for providing quality service.
Another exemplary use of the above described databases is data mining that semiautomatically extracts certain information by analyzing a large volume of database data. In particular, data mining includes rule induction, Memory Based Reasoning (MBR), On-Line Analytical Processing (OLAP), and the these exemplary data mining methods are disclosed in “Data Mining Techniques For Marketing, Sales and Customer Support,” pp. 120-123, John Wiley & Sons, Inc (1997). Rule induction generally extracts certain International Conference on Systems, Man, and Cybernetics,” p.V.-882-886. For one example of MBR, as disclosed in the above “Data Mining Techniques For Marketing, Sales and Customer Support” at p.120, a certain future event is evaluated based upon similar to a known event in the database. For example, the occurrence of the future event is quantified based upon the known similar event or the future event is classified based upon the known similar event. Finally, for OLAP, as disclosed in the above “Data Mining Techniques For Marketing, Sales and Customer Support” at p.123, a significant pattern in the data is explored, and the result is displayed based upon a multidimensional database. By combining the induction rule and OLAP techniques, one way to improve the precision of the MBR-based prediction is disclosed in “Customer Relationship Management Through Data Mining,” Proceedings of Informs Seoul, P1956-1963, (2001).
In the above described combination of prior art, the last exemplary prior art is designed to predict or speculate on a certain segment of the data based upon a predetermined rule. However, in the last exemplary prior art, a user is not able to specify an additional rule and or to delete any existing rules based upon his or her opinion or other circumstances. The user is not able to ascertain certain characteristics of the segment such as a number of customers. For the above reasons, it is desired that a user specifies an additional rule and or to delete any existing rules based upon his or her opinion or other circumstances to ascertain certain characteristics of the data segment. It is also desired to display or identify any user-specified conditions on the results.